Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data

被引：280

作者：

Benchimol, Eric I. ^{[1
,2
,3
,4
,5
,6
]}

Manuel, Douglas G. ^{[2
,7
,8
,9
,10
]}

To, Teresa ^{[2
,4
]}

Griffiths, Anne M. ^{[3
,5
]}

Rabeneck, Linda ^{[2
,4
,11
]}

Guttmann, Astrid ^{[2
,4
,5
,12
]}

机构：

[1] Childrens Hosp Eastern Ontario, Div Gastroenterol Hepatol & Nutr, Ottawa, ON K1H 8L1, Canada

[2] Inst Clin Evaluat Sci, Toronto, ON, Canada

[3] Hosp Sick Children, Div Gastroenterol Hepatol & Nutr, Toronto, ON M5G 1X8, Canada

[4] Univ Toronto, Dept Hlth Policy Management & Evaluat, Toronto, ON, Canada

[5] Univ Toronto, Dept Paediat, Toronto, ON M5S 1A1, Canada

[6] Univ Ottawa, Dept Pediat, Ottawa, ON K1N 6N5, Canada

[7] Univ Ottawa, Dept Family Med, Ottawa, ON, Canada

[8] Univ Ottawa, Dept Epidemiol & Community Med, Ottawa, ON, Canada

[9] Ottawa Hosp Res Inst, Ottawa, ON, Canada

[10] Stat Canada, Ottawa, ON, Canada

[11] Univ Toronto, Dept Med, Toronto, ON, Canada

[12] Hosp Sick Children, Div Paediat Med, Toronto, ON M5G 1X8, Canada

来源：

JOURNAL OF CLINICAL EPIDEMIOLOGY | 2011年 / 64卷 / 08期

关键词：

Health administrative data; Misclassification bias; Diagnostic accuracy; Sensitivity and specificity; Predictive values; Health services research; Epidemiology; PREDICTIVE-VALUE; PREVALENCE; DISEASE; ASTHMA; MISCLASSIFICATION; EPIDEMIOLOGY; ALGORITHM; ACCURACY;

D O I：

10.1016/j.jclinepi.2010.10.006

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

100404 [儿少卫生与妇幼保健学];

摘要：

Background and Objectives: Validation of health administrative data for identifying patients with different health states (diseases and conditions) is a research priority, but no guidelines exist for ensuring quality. We created reporting guidelines for studies validating administrative data identification algorithms and used them to assess the quality of reporting of validation studies in the literature. Methods: Using Standards for Reporting of Diagnostic accuracy (STARD) criteria as a guide, we created a 40-item checklist of items with which identification accuracy studies should be reported. A systematic review identified studies that validated identification algorithms using administrative data. We used the checklist to assess the quality of reporting. Results: In 271 included articles, goals and data sources were well reported but few reported four or more statistical estimates of accuracy (36.9%). In 65.9% of studies reporting positive predictive value (PPV)/negative predictive value (NPV), the prevalence of disease in the validation cohort was higher than in the administrative data, potentially falsely elevating predictive values. Subgroup accuracy (53.1%) and 95% confidence intervals for accuracy measures (35.8%) were also underreported. Conclusions: The quality of studies validating health states in the administrative data varies, with significant deficits in reporting of markers of diagnostic accuracy, including the appropriate estimation of PPV and NPV. These omissions could lead to misclassification bias and incorrect estimation of incidence and health services utilization rates. Use of a reporting checklist, such as the one created for this study by modifying the STARD criteria, could improve the quality of reporting of validation studies, allowing for accurate application of algorithms, and interpretation of research using health administrative data. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：821 / 829

页数：9

共 34 条

[1]

Preferred provider organization claims showed high predictive value but missed substantial proportion of adults with high-risk conditions [J].